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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
SAR studies the intricate relationship between a drug's chemical structure and biological activity. It focuses on understanding how modifications to a drug's structure can influence...
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Local Anesthetics: Chemistry and Structure-Activity Relationship01:30

Local Anesthetics: Chemistry and Structure-Activity Relationship

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Local anesthetics (LAs) are drugs that induce a temporary loss of sensation in a limited body area, preventing pain. Cocaine was the first local anesthetic discovered in the late 19th century. Cocaine is a benzoic acid ester obtained from the leaves of coca shrubs and was often used for its psychotropic effects. Cocaine was first isolated in 1860 by Albert Niemann. Sigmund Freud studied the physiological actions of cocaine. Carl Koller later introduced it into clinical practice in 1884 as a...
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Cholinergic Antagonists: Chemistry and Structure-Activity Relationship01:29

Cholinergic Antagonists: Chemistry and Structure-Activity Relationship

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Cholinergic antagonists bind to cholinergic receptors and limit the effects of acetylcholine and other cholinergic agonists. Based on the specific cholinergic receptor affinity, these antagonists are classified as muscarinic or nicotinic. Anticholinergics interrupt parasympathetic innervations while sympathetic innervations remain uninterrupted. Muscarinic antagonists are also called 'muscarinic antagonists', 'antimuscarinics', or 'parasympatholytics'. Nicotinic...
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Adrenergic Agonists: Chemistry and Structure-Activity Relationship01:16

Adrenergic Agonists: Chemistry and Structure-Activity Relationship

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Adrenergic agonists' structure-activity relationship (SAR) determines their selectivity and efficacy. These agonists comprise a phenylethylamine moiety with an aromatic ring and an ethylamine side chain.
Aromatic ring substitutions: Substituting the aromatic ring with –OH groups at positions 3 and 4 yields catecholamines (e.g., epinephrine), which have a high affinity for adrenoceptors. Hydrogen bonding between –OH groups and receptors enhances adrenergic activity.
Separation of...
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Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:29

Indirect-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

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Indirect-acting cholinergic agonists are agents that interact with the acetylcholinesterase enzyme in the synaptic cleft, preventing the breakdown of acetylcholine into choline and acetate. Consequently, the concentration of acetylcholine in the synaptic cleft increases. These agonists can be classified into reversible and irreversible inhibitors based on their duration of action.
Reversible inhibitors display short to medium durations of action. Short-acting agents include simple alcohols with...
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Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship01:22

Direct-Acting Cholinergic Agonists: Chemistry and Structure-Activity Relationship

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Cholinergic agonists or cholinomimetics mimic the action of acetylcholine to stimulate the parasympathetic nervous system. They are categorized into direct-acting and indirect-acting agents. The direct-acting cholinergic drugs induce the parasympathetic response by directly binding to the muscarinic or nicotine receptors. In comparison, the indirect-acting cholinergic drugs prevent acetylcholine hydrolysis, indirectly contributing to the extended parasympathetic response.
The direct-acting...
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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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Quantitative structure-activity relationship analysis using deep learning based on a novel molecular image input

Yoshihiro Uesawa1

  • 1Department of Medical Molecular Informatics, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588, Japan.

Bioorganic & Medicinal Chemistry Letters
|September 5, 2018
PubMed
Summary

Deep learning with 360° molecular images advances quantitative structure-activity relationship (QSAR) analysis. This approach accurately predicts compounds disrupting mitochondrial membrane potential, achieving high external validation performance.

Keywords:
Deep learningIn silicoMitochondrial membrane potential disruptionMolecular imageryQuantitative structure–activity relationshipThree-dimensional structure

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Area of Science:

  • Computational chemistry
  • cheminformatics
  • toxicology

Background:

  • Quantitative structure-activity relationship (QSAR) models predict biological activity using molecular features.
  • Deep learning, particularly artificial neural networks, shows promise in QSAR research due to its feature representation learning capabilities.
  • Current deep learning applications in QSAR are limited by the direct calculation of feature values from molecular structure.

Purpose of the Study:

  • To apply deep learning with feature representation learning to QSAR analysis.
  • To incorporate 360° images of molecular conformations into deep learning models for QSAR.
  • To develop a versatile identification model for chemical compounds that induce mitochondrial membrane potential disruption.

Main Methods:

  • Utilized deep learning, specifically advanced artificial neural networks.
  • Incorporated 360° images of molecular conformations as input data.
  • Applied feature representation learning directly from molecular structure.
  • Validated the model using external datasets.

Main Results:

  • Successfully constructed a highly versatile identification model for chemical compounds.
  • The model accurately predicts compounds that disrupt mitochondrial membrane potential.
  • Achieved an area under the receiver operating characteristic curve (AUC) of ≥0.9 in external validation.

Conclusions:

  • Deep learning with 360° molecular images is a powerful approach for QSAR modeling.
  • This method enhances the prediction of compound-induced mitochondrial dysfunction.
  • The developed model demonstrates high accuracy and versatility for identifying toxic compounds.